{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Q = 2000/3    #strength of the source-sheet,stb/d\n",
    "Dx=9.8425\n",
    "Dy=9.8425\n",
    "h=26.25        #thickness of local gridblock,ft\n",
    "phi=0.2        #porosity \n",
    "kx=200         #pemerability in x direction,md\n",
    "ky=200        #pemerability in y direction,md\n",
    "kr=kx/ky       #pemerability ratio\n",
    "miu=1          #viscosity,cp\n",
    "\n",
    "Nw=1                    #Number of well\n",
    "Nimg=40                 #Number of image well\n",
    "rab=0.00003       #distance betwwen node pairs\n",
    "Qwell_1=2000            #Flow rate of well 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Panel:\n",
    "    \"\"\"Contains information related to a panel.\"\"\"\n",
    "    def __init__(self, xa, ya, xb, yb):\n",
    "        \"\"\"Creates a panel.\n",
    "        \n",
    "        Arguments\n",
    "        ---------\n",
    "        xa, ya -- Cartesian coordinates of the first end-point.\n",
    "        xb, yb -- Cartesian coordinates of the second end-point.\n",
    "        x_a,y_a -- node pair A, inner point \n",
    "        y_b,\n",
    "        \"\"\"\n",
    "        self.xa, self.ya = xa, ya\n",
    "        self.xb, self.yb = xb, yb\n",
    "        \n",
    "        self.xc, self.yc = (xa+xb)/2, (ya+yb)/2            # control-point (center-point)\n",
    "        self.length = math.sqrt((xb-xa)**2+(yb-ya)**2)     # length of the panel\n",
    "        \n",
    "        #unit vector (x2-x1)/length  (y2-y1)/length\n",
    "        #normal unit vector (y1-y2)/length   (x2-x1)/length\n",
    "        #point with a given distance along a line: x?=x_Start+unit_vector_x*distance  y?=y_Start+unit_vector_y*distance\n",
    "        self.x_a=self.xc-rab/2*(ya-yb)/self.length\n",
    "        self.y_a=self.yc-rab/2*(xb-xa)/self.length\n",
    "        self.x_b=self.xc+rab/2*(ya-yb)/self.length\n",
    "        self.y_b=self.yc+rab/2*(xb-xa)/self.length\n",
    "        \n",
    "        # orientation of the panel (angle between x-axis and panel)\n",
    "        self.sinalpha=(yb-ya)/self.length\n",
    "        self.cosalpha=(xb-xa)/self.length\n",
    "        \n",
    "        self.Q = 0.                                # source strength\n",
    "        self.U = 0.                                # velocity component\n",
    "        self.V = 0.                                # velocity component\n",
    "        self.P = 0.                                # pressure coefficient\n",
    "\n",
    "class Well:\n",
    "    \"\"\"Contains information related to a panel.\"\"\"\n",
    "    def __init__(self, xw, yw,rw,Q):\n",
    "        \"\"\"Creates a panel.\n",
    "        \n",
    "        Arguments\n",
    "        ---------\n",
    "        xw, yw -- Cartesian coordinates of well source.\n",
    "        Q      -- Flow rate of well source.\n",
    "        rw     -- radius of well source.\n",
    "        \"\"\"\n",
    "        self.xw, self.yw = xw, yw\n",
    "        \n",
    "        self.Q = Q                               # source strength\n",
    "        self.rw = rw                             # well radius\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def cosspace(st,ed,N):\n",
    "    N=N+1\n",
    "    AngleInc=numpy.pi/(N-1)\n",
    "    CurAngle = AngleInc\n",
    "    space=numpy.linspace(0,1,N)\n",
    "    space[0]=st\n",
    "    for i in range(N-1):\n",
    "        space[i+1] = 0.5*numpy.abs(ed-st)*(1 - math.cos(CurAngle));\n",
    "        CurAngle += AngleInc\n",
    "    if ed<st:\n",
    "        space[0]=ed\n",
    "        space=space[::-1]\n",
    "    return space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Create the array\n",
    "N=80\n",
    "Nbd=20\n",
    "\n",
    "\n",
    "x_ends = numpy.linspace(0, 1, N)    # computes a 1D-array for x\n",
    "x_ends_img= numpy.linspace(0, 1, N)\n",
    "y_ends = numpy.linspace(0, 1, N)    # computes a 1D-array for y\n",
    "y_ends_img= numpy.linspace(0, 1, N)\n",
    "\n",
    "\n",
    "interval=cosspace(0,Dx,Nbd)\n",
    "rinterval=cosspace(Dx,0,Nbd)\n",
    "#interval=numpy.linspace(0,Dx,Nbd+1)\n",
    "#rinterval=numpy.linspace(Dx,0,Nbd+1)\n",
    "\n",
    "#Define the rectangle boundary\n",
    "\n",
    "\n",
    "for i in range(Nbd):\n",
    "    x_ends[i]=0\n",
    "    y_ends[i]=interval[i]\n",
    "\n",
    "for i in range(Nbd):\n",
    "    x_ends[i+Nbd]=interval[i]\n",
    "    y_ends[i+Nbd]=Dy\n",
    "    \n",
    "for i in range(Nbd):\n",
    "    x_ends[i+Nbd*2]=Dx\n",
    "    y_ends[i+Nbd*2]=rinterval[i]\n",
    "    \n",
    "for i in range(Nbd):\n",
    "    x_ends[i+Nbd*3]=rinterval[i]\n",
    "    y_ends[i+Nbd*3]=0\n",
    "    \n",
    "x_ends,y_ends=numpy.append(x_ends, x_ends[0]), numpy.append(y_ends, y_ends[0])\n",
    "\n",
    "\n",
    "#Define the panel/node pairs\n",
    "panels = numpy.empty(N, dtype=object)\n",
    "\n",
    "for i in range(N):\n",
    "    panels[i] = Panel(x_ends[i], y_ends[i], x_ends[i+1], y_ends[i+1])\n",
    "\n",
    "    \n",
    "#Define boundary conditions\n",
    "for i in range(Nbd):\n",
    "    panels[i].Q=0.\n",
    "    panels[i+Nbd].Q=1000.\n",
    "    panels[i+Nbd*2].Q=1000.\n",
    "    panels[i+Nbd*3].Q=0.\n",
    "    \n",
    "# Generalize the image well position/Define the image panel around the computation domain\n",
    "R=Dx\n",
    "x_ends = Dx/2+R*numpy.cos(numpy.linspace(0, 2*math.pi, Nimg+1))\n",
    "y_ends = Dy/2+R*numpy.sin(numpy.linspace(0, 2*math.pi, Nimg+1))\n",
    "\n",
    "panels_img = numpy.empty(Nimg, dtype=object)\n",
    "for i in range(Nimg):\n",
    "    panels_img[i] = Panel(x_ends[i], y_ends[i], x_ends[i+1], y_ends[i+1])\n",
    "\n",
    "    \n",
    "#Define the well\n",
    "wells = numpy.empty(Nw, dtype=object)\n",
    "\n",
    "wells[0]=Well(Dx/2,Dy/2,0.025,Qwell_1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#for i in range(N):\n",
    "    #print(\"%s Panel Coordinate (%s,%s) sina,cosa (%s,%s) Q:%s\" % (i+1,panels[i].xc,panels[i].yc,panels[i].sinalpha,panels[i].cosalpha,panels[i].Q))\n",
    "   # print(\"%s NodePair Coordinate A(%s,%s) B(%s,%s) Q:%s\" % (i+1,panels[i].x_a,panels[i].y_a,panels[i].x_b,panels[i].y_b,panels[i].Q))\n",
    "    #print(\"Well Location (%s,%s) radius: %s Flow rate:%s  \" % (wells[0].xw,wells[0].yw,wells[0].rw,wells[0].Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x9a9f278>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MyJEj2bt3r/P5//nPf1izZg1r1qzhpZde4vDhwwAcP36cfv36sX79ev7v//6PnTt38ttv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\nhPrL+rG2GrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x89cf080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the panel\n",
    "%matplotlib inline\n",
    "\n",
    "val_x, val_y = 1.2, 1.2\n",
    "x_min, x_max = min(panel.xa for panel in panels), max(panel.xa for panel in panels)\n",
    "y_min, y_max = min(panel.ya for panel in panels), max(panel.ya for panel in panels)\n",
    "x_start, x_end = x_min-val_x*(x_max-x_min), x_max+val_x*(x_max-x_min)\n",
    "y_start, y_end = y_min-val_y*(y_max-y_min), y_max+val_y*(y_max-y_min)\n",
    "\n",
    "size = 7\n",
    "pyplot.figure(figsize=(size, (y_end-y_start)/(x_end-x_start)*size))\n",
    "pyplot.grid(True)\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "pyplot.xlim(x_start, x_end)\n",
    "pyplot.ylim(y_start, y_end)\n",
    "\n",
    "pyplot.plot(numpy.append([panel.xa for panel in panels], panels[0].xa), \n",
    "         numpy.append([panel.ya for panel in panels], panels[0].ya), \n",
    "         linestyle='-', linewidth=1, marker='o', markersize=0, color='#CD2305');\n",
    "\n",
    "pyplot.plot(numpy.append([panel.xa for panel in panels_img], panels_img[0].xa), \n",
    "         numpy.append([panel.ya for panel in panels_img], panels_img[0].ya), \n",
    "         linestyle='-', linewidth=1, marker='o', markersize=6, color='r');\n",
    "\n",
    "\n",
    "#pyplot.scatter([w.xw for w in Image_wells], [w.yw for w in Image_wells], color='#CD2305', s=40)\n",
    "pyplot.scatter([p.x_a for p in panels], [p.y_a for p in panels], color='b', s=40)\n",
    "pyplot.scatter([p.x_b for p in panels], [p.y_b for p in panels], color='b', s=40)\n",
    "\n",
    "\n",
    "pyplot.scatter(wells[0].xw,wells[0].yw,s=100,alpha=0.5)\n",
    "\n",
    "pyplot.legend(['Interest domain','panels','Boundary node pair A','Boundary node pair B','Wells'], \n",
    "           loc=1, prop={'size':10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Panel infuence factor\n",
    "def InflueceP(x, y, panel):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "#Transfer global coordinate point(x,y) to local coordinate\n",
    "    x=x-panel.xa\n",
    "    y=y-panel.ya\n",
    "    L1=panel.length\n",
    "    \n",
    "#Calculate the pressure and velocity influence factor\n",
    "    a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
    "    b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
    "    c=y*panel.cosalpha-x*panel.sinalpha\n",
    "    dp=-70.6*miu/h/math.sqrt(kx*ky)\n",
    "    Cp = -1/a*(\n",
    "                             (\n",
    "                              b*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)\n",
    "                             -L1*a*math.log((x-L1*panel.cosalpha)**2+kr*(y-L1*panel.sinalpha)**2)\n",
    "                             +2*math.sqrt(kr)*c*math.atan((b-a*L1)/math.sqrt(kr)/c)\n",
    "                             )\n",
    "                             -\n",
    "                             (\n",
    "                               b*math.log(x**2+kr*y**2)\n",
    "                               +2*math.sqrt(kr)*c*math.atan((b)/math.sqrt(kr)/c)\n",
    "                             )         \n",
    "                )\n",
    "    #debug\n",
    "    #print(\"a: %s b:%s c:%s  \" % (a,b,c))\n",
    "    #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
    "    #print(\"Magic angle:%s\"% angle)\n",
    "    return Cp\n",
    "\n",
    "def InflueceU(x, y, panel):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "#Transfer global coordinate point(x,y) to local coordinate\n",
    "    x=x-panel.xa\n",
    "    y=y-panel.ya\n",
    "    L1=panel.length\n",
    "\n",
    "#Calculate the pressure and velocity influence factor\n",
    "    a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
    "    b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
    "    c=y*panel.cosalpha-x*panel.sinalpha\n",
    "    dv=-0.4468/h/phi*math.sqrt(kx/ky)\n",
    "    Cu = dv/a*(\n",
    "                             ( \n",
    "                            panel.cosalpha*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)+ 2*math.sqrt(kr)*panel.sinalpha*math.atan((a*L1-b)/math.sqrt(kr)/c) \n",
    "                             )\n",
    "                            -\n",
    "                             (\n",
    "                            panel.cosalpha*math.log(x**2+kr*y**2)+2*math.sqrt(kr)*panel.sinalpha*math.atan((-b)/math.sqrt(kr)/c)\n",
    "                             )    \n",
    "                     )  \n",
    "    #print(\"a: %s b:%s c:%s  \" % (a,b,c))\n",
    "    #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
    "    #print(\"Magic angle:%s\"% angle)\n",
    "    return Cu\n",
    "\n",
    "def InflueceV(x, y, panel):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "#Transfer global coordinate point(x,y) to local coordinate\n",
    "    x=x-panel.xa\n",
    "    y=y-panel.ya\n",
    "    L1=panel.length\n",
    "\n",
    "#Calculate the pressure and velocity influence factor\n",
    "    a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
    "    b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
    "    c=y*panel.cosalpha-x*panel.sinalpha\n",
    "    dv=-0.4468/h/phi*math.sqrt(kx/ky)\n",
    "    Cv = dv/a*(\n",
    "                             ( \n",
    "                            panel.sinalpha*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)+ 2*math.sqrt(1/kr)*panel.cosalpha*math.atan((b-a*L1)/math.sqrt(kr)/c) \n",
    "                             )\n",
    "                            -\n",
    "                             (\n",
    "                            panel.sinalpha*math.log(x**2+kr*y**2)+2*math.sqrt(1/kr)*panel.cosalpha*math.atan((b)/math.sqrt(kr)/c)\n",
    "                             )    \n",
    "                     )    \n",
    "    #print(\"a: %s b:%s c:%s  \" % (a,b,c))\n",
    "    #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
    "    #print(\"Magic angle:%s\"% angle)\n",
    "\n",
    "    return Cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Well influence factor\n",
    "def InflueceP_W(x, y, well):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "    dp=-70.6*miu/h/math.sqrt(kx*ky)\n",
    "    #Cp=dp*math.log((x-well.xw)**2+kr*(y-well.yw)**2)\n",
    "    Cp=math.log((x-well.xw)**2+kr*(y-well.yw)**2)\n",
    "    return Cp\n",
    "\n",
    "def InflueceU_W(x, y, well):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "    dv=0.8936/h/phi*math.sqrt(kx/ky)\n",
    "    Cu=dv*(x-well.xw)/((x-well.xw)**2+kr*(y-well.yw)**2)\n",
    "    return Cu\n",
    "\n",
    "def InflueceV_W(x, y, well):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "    dv=0.8936/h/phi*math.sqrt(kx/ky)\n",
    "    Cv=dv*(y-well.yw)/((x-well.xw)**2+kr*(y-well.yw)**2)\n",
    "    return Cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_matrix(panels):\n",
    "    \"\"\"Builds the source matrix.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    panels -- array of panels.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    A -- NxN matrix (N is the number of panels).\n",
    "    \"\"\"\n",
    "    N = len(panels)\n",
    "    A = numpy.empty((N, Nimg), dtype=float)\n",
    "    #numpy.fill_diagonal(A, 0.5)\n",
    "    Coeff=-70.6*miu/h/math.sqrt(kx*ky)\n",
    "    \n",
    "    for i, p_i in enumerate(panels): #target nodes\n",
    "        for j, p_j in enumerate(panels_img): #BE source\n",
    "                #if i>=0 and i<Nbd or i>=3*Nbd and i<4*Nbd:     \n",
    "                    A[i,j] = InflueceP(p_i.x_a,p_i.y_a,p_j)-InflueceP(p_i.x_b,p_i.y_b,p_j)\n",
    "                    #A[i,j] /= Coeff\n",
    "                    #A[i,j] = InflueceP(p_i.xc, p_i.yc, p_j)\n",
    "                #if i>=Nbd and i<2*Nbd or i>=2*Nbd and i<3*Nbd:     \n",
    "                    #A[i,j] = -p_j.sinalpha*InflueceU(p_i.xc, p_i.yc, p_j)+p_j.cosalpha*InflueceV(p_i.xc, p_i.yc, p_j)\n",
    "                    #A[i,j] = InflueceP(p_i.xc, p_i.yc, p_j)\n",
    "    return A\n",
    "\n",
    "def build_rhs(panels):\n",
    "    \"\"\"Builds the RHS of the linear system.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    panels -- array of panels.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    b -- 1D array ((N+1)x1, N is the number of panels).\n",
    "    \"\"\"\n",
    "\n",
    "    b = numpy.empty(len(panels), dtype=float)\n",
    "    \n",
    "    for i, p_i in enumerate(panels):\n",
    "        #b[i]=Qwell_1*( InflueceP_W(p_i.x_b,p_i.y_b,wells[0])-InflueceP_W(p_i.x_a,p_i.y_a,wells[0]) )+887.1857*p_i.Q*miu*rab/Dx/h/kx\n",
    "        #b[i] /= Coeff\n",
    "        b[i]=Qwell_1*( InflueceP_W(p_i.x_b,p_i.y_b,wells[0])-InflueceP_W(p_i.x_a,p_i.y_a,wells[0]) )-4*numpy.pi*p_i.Q*rab*math.sqrt(kx*ky)/Dx/kx\n",
    "        #b[i]=887.2*p_i.Q*miu*rab/Dx/h\n",
    "    return b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = build_matrix(panels)                    # computes the singularity matrix\n",
    "b = build_rhs(panels)                       # computes the freestream RHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03830237423731524"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4*numpy.pi*1000*rab*math.sqrt(kx*ky)/Dx/kx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013447619047619047"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "70.6*miu/h/math.sqrt(kx*ky)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01226731,  0.01257115,  0.0131927 ,  0.01415603,  0.0154853 ,\n",
       "        0.01718171,  0.01918004,  0.02128598,  0.02313123,  0.02423578,\n",
       "        0.02423578,  0.02313123,  0.02128598,  0.01918004,  0.01718171,\n",
       "        0.0154853 ,  0.01415603,  0.0131927 ,  0.01257115,  0.01226731,\n",
       "       -0.02603507, -0.02573122, -0.02510968, -0.02414634, -0.02281707,\n",
       "       -0.02112066, -0.01912233, -0.0170164 , -0.01517114, -0.0140666 ,\n",
       "       -0.0140666 , -0.01517114, -0.0170164 , -0.01912233, -0.02112066,\n",
       "       -0.02281707, -0.02414634, -0.02510968, -0.02573122, -0.02603507,\n",
       "       -0.02603507, -0.02573122, -0.02510968, -0.02414634, -0.02281707,\n",
       "       -0.02112066, -0.01912233, -0.0170164 , -0.01517114, -0.0140666 ,\n",
       "       -0.0140666 , -0.01517114, -0.0170164 , -0.01912233, -0.02112066,\n",
       "       -0.02281707, -0.02414634, -0.02510968, -0.02573122, -0.02603507,\n",
       "        0.01226731,  0.01257115,  0.0131927 ,  0.01415603,  0.0154853 ,\n",
       "        0.01718171,  0.01918004,  0.02128598,  0.02313123,  0.02423578,\n",
       "        0.02423578,  0.02313123,  0.02128598,  0.01918004,  0.01718171,\n",
       "        0.0154853 ,  0.01415603,  0.0131927 ,  0.01257115,  0.01226731])"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -5.48620784e-06,  -5.13742042e-06,  -4.79108551e-06, ...,\n",
       "         -6.57866625e-06,  -6.20378275e-06,  -5.84062554e-06],\n",
       "       [ -5.51609588e-06,  -5.17136312e-06,  -4.82794292e-06, ...,\n",
       "         -6.58718696e-06,  -6.22128918e-06,  -5.86512622e-06],\n",
       "       [ -5.57416234e-06,  -5.23800554e-06,  -4.90086485e-06, ...,\n",
       "         -6.60122267e-06,  -6.25367950e-06,  -5.91195013e-06],\n",
       "       ..., \n",
       "       [ -2.21831080e-06,  -2.66860653e-06,  -3.08672571e-06, ...,\n",
       "         -5.74004808e-07,  -1.18386560e-06,  -1.72711371e-06],\n",
       "       [ -2.15602113e-06,  -2.59854404e-06,  -3.01129494e-06, ...,\n",
       "         -5.54691184e-07,  -1.14622139e-06,  -1.67542801e-06],\n",
       "       [ -2.12543369e-06,  -2.56403619e-06,  -2.97402358e-06, ...,\n",
       "         -5.45282489e-07,  -1.12783782e-06,  -1.65011957e-06]])"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# solves the linear system\n",
    "Q = numpy.linalg.lstsq(A, b)\n",
    "\n",
    "for i in range(Nimg):\n",
    "    panels_img[i].Q =Q[0][i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 368113.95693063,  366975.33220918,  367382.42691494,\n",
       "        367257.09359321,  367354.26031266,  367354.14805368,\n",
       "        367257.25979982,  367382.14919629,  366975.76272258,\n",
       "        368113.38460663,  368129.86014543,  367022.70159778,\n",
       "        367460.32616438,  367363.12016918,  367486.34987881,\n",
       "        367508.5620862 ,  367430.60788185,  367569.78998256,\n",
       "        367173.45610797,  368315.83657617,  368332.53342319,\n",
       "        367220.25182775,  367648.02526091,  367536.45903223,\n",
       "        367640.74999845,  367640.68406678,  367536.56667225,\n",
       "        367647.82902429,  367220.58020678,  368332.06454985,\n",
       "        368316.38766266,  367172.92836581,  367570.21606541,\n",
       "        367430.28818696,  367508.82159279,  367486.08643129,\n",
       "        367363.45446545,  367459.86753422,  367023.28608467,\n",
       "        368129.22523935])"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#for i in range(Nimg):\n",
    "    #print(\"%s Image well flow rate:%s\" %(i+1,Image_wells[i].Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Visulize the pressure and velocity field\n",
    "\n",
    "#Define meshgrid\n",
    "\n",
    "\n",
    "\n",
    "Nx, Ny = 50, 50                  # number of points in the x and y directions\n",
    "val_x, val_y = 0, 0\n",
    "x_min, x_max = min(panel.xa for panel in panels), max(panel.xa for panel in panels)\n",
    "y_min, y_max = min(panel.ya for panel in panels), max(panel.ya for panel in panels)\n",
    "x_start, x_end = x_min-val_x*(x_max-x_min), x_max+val_x*(x_max-x_min)\n",
    "y_start, y_end = y_min-val_y*(y_max-y_min), y_max+val_y*(y_max-y_min)\n",
    "x = numpy.linspace(x_start, x_end, Nx)    # computes a 1D-array for x\n",
    "y = numpy.linspace(y_start, y_end, Ny)    # computes a 1D-array for y\n",
    "X, Y = numpy.meshgrid(x, y)               # generates a mesh grid\n",
    "\n",
    "#Calculate the velocity and pressure field\n",
    "p = numpy.empty((Nx, Ny), dtype=float)\n",
    "u = numpy.empty((Nx, Ny), dtype=float)\n",
    "v = numpy.empty((Nx, Ny), dtype=float)\n",
    "\n",
    "Coeff=-70.6*miu/h/math.sqrt(kx*ky)\n",
    "\n",
    "\n",
    "\n",
    "for i in range(Nx):\n",
    "    for j in range(Ny):\n",
    "        p[i,j] =sum([w.Q*Coeff*InflueceP(X[i,j], Y[i,j], w) for w in panels_img])+Qwell_1*Coeff*InflueceP_W(X[i,j], Y[i,j], wells[0])-160\n",
    "        u[i,j] =sum([w.Q*InflueceU(X[i,j], Y[i,j], w) for w in panels_img])+Qwell_1*InflueceU_W(X[i,j], Y[i,j], wells[0])\n",
    "        v[i,j] =sum([w.Q*InflueceV(X[i,j], Y[i,j], w) for w in panels_img])+Qwell_1*InflueceV_W(X[i,j], Y[i,j], wells[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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05i1aOKvdmEGkV69CJy9i42zYgGutX68/3Ld2LYh+40a1z/n5cDetUgULl3hv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VVlqzxrjiZlrCvWSrWdy7hyBYsgQN8KOPfJtcKRX0cXGM06JFIoGBbKrlyuGGcS5k5w6rlYVx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\nTNgbhmHEACbsDcMwYoD/BzwKzah9ytDCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13439278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots the streamlines\n",
    "%matplotlib inline\n",
    "\n",
    "size = 6\n",
    "pyplot.figure(figsize=(size, size))\n",
    "pyplot.grid(True)\n",
    "pyplot.title('Streamline Plot')\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "pyplot.xlim(x_start, x_end)\n",
    "pyplot.ylim(y_start, y_end)\n",
    "\n",
    "\n",
    "pyplot.plot(numpy.append([panel.xa for panel in panels], panels[0].xa), \n",
    "         numpy.append([panel.ya for panel in panels], panels[0].ya), \n",
    "         linestyle='-', linewidth=1, marker='o', markersize=6, color='#CD2305');\n",
    "stream =pyplot.streamplot(X, Y, u, v,density=2, linewidth=1, arrowsize=1, arrowstyle='->') #streamline\n",
    "#cbar=pyplot.colorbar(orientation='vertical')\n",
    "\n",
    "#equipotential=pyplot.contourf(X, Y, p1, extend='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11bcdeb8>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HIyO5CE9pOXsqzJF+py5RfXoD/sg/M6ylkrh2lP/9QVwn3Yclzrj1aWKzEzX9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14be6438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = 7\n",
    "pyplot.figure(figsize=(size, size-1))\n",
    "pyplot.title('Pressure field (psi)')\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "pyplot.xlim(x_start, x_end)\n",
    "pyplot.ylim(y_start, y_end)\n",
    "\n",
    "pyplot.contour(X, Y, p, 15,linewidths=0.5, colors='k')\n",
    "pyplot.contourf(X, Y, p, 15,cmap='rainbow',)\n",
    "pyplot.colorbar()  # draw colorbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0xe6e9828>"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/RP1/fwIj0vveU70RizURsT0PfBFQT0kGkfY1L5M5Yde3h4i3NRG94xYiH7+q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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146d7358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = 7\n",
    "pyplot.figure(figsize=(size, size-1.5))\n",
    "pyplot.title('Total Velocity field (ft/day)')\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "pyplot.xlim(x_start, x_end)\n",
    "pyplot.ylim(y_start, y_end)\n",
    "\n",
    "Vtotal= numpy.sqrt(u**2+v**2)\n",
    "#Vtotal= numpy.abs(v)\n",
    "pyplot.contour(X, Y, Vtotal, 15, linewidths=0.5, colors='k')\n",
    "pyplot.contourf(X, Y, Vtotal, 15, cmap='rainbow')\n",
    "                  #vmax=110, vmin=0)\n",
    "pyplot.colorbar()  # draw colorbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 108.65833288,  108.65607892,  108.65757825,  108.65890791,\n",
       "        108.65858304,  108.65711671,  108.65562429,  108.65490779,\n",
       "        108.65519665,  108.65626637,  108.65766298,  108.6588998 ,\n",
       "        108.65959481,  108.65955262,  108.65879533,  108.65754203,\n",
       "        108.65614227,  108.6549795 ,  108.65436921,  108.65447781,\n",
       "        108.65528186,  108.65657669,  108.65803067,  108.65927116,\n",
       "        108.65998012,  108.65997567,  108.65925861,  108.65801236,\n",
       "        108.65655601,  108.65526272,  108.65446388,  108.65436332,\n",
       "        108.65498307,  108.65615509,  108.65756226,  108.65881985,\n",
       "        108.6595776 ,  108.6596164 ,  108.65891488,  108.65766969,\n",
       "        108.6562644 ,  108.65518717,  108.65489298,  108.65560677,\n",
       "        108.65709882,  108.65856636,  108.65889295,  108.6575648 ,\n",
       "        108.65606757,  108.65832956])"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PX9mdy+XI5VqajYhI/WloaKChoSH17624azpmNgE4AlgGrAX0ILwuYScg5+6N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\n0hERkdQo6YiISGr+P0nkjGCGf6SEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119560f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.title('Darcy velocity on the outflow boundary, x component (ft/day)')\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "\n",
    "pyplot.plot(y, u[49,:], '--', linewidth=2)\n",
    "pyplot.plot(9.8425+y, u[:,49], '--', linewidth=2)\n",
    "u[:,49]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 108.65829189,  108.65605024,  108.65756994,  108.65891554,\n",
       "        108.65859741,  108.65712906,  108.65562905,  108.65490339,\n",
       "        108.65518495,  108.65625135,  108.65764921,  108.65889113,\n",
       "        108.65959348,  108.65955892,  108.65880774,  108.65755776,\n",
       "        108.65615798,  108.65499207,  108.65437634,  108.65447841,\n",
       "        108.65527615,  108.65656601,  108.65801712,  108.65925707,\n",
       "        108.65996756,  108.659966  ,  108.65925229,  108.65800888,\n",
       "        108.65655409,  108.65526065,  108.65445997,  108.65435631,\n",
       "        108.65497251,  108.65614146,  108.65754695,  108.65880484,\n",
       "        108.65956506,  108.65960812,  108.65891177,  108.65767144,\n",
       "        108.65626938,  108.65519265,  108.65489575,  108.65560387,\n",
       "        108.65708848,  108.65854862,  108.65886992,  108.65753997,\n",
       "        108.6560442 ,  108.65830794])"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oSYiISColCRERSaUk0QE8+ii8/nrWUYiINKYkkbF16+DYY8OT1XPmZB2NiEhD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\nDwpKWBO9eO4xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1491bc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.title('Darcy velocity on the outflow boundary, y component (ft/day)')\n",
    "\n",
    "pyplot.plot(y, v[:,49], '--', linewidth=2)\n",
    "pyplot.plot(9.8425+y, v[49,:], '--', linewidth=2)\n",
    "v[49,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
